{"id":"W4225376695","doi":"10.1186/s12859-022-04673-3","title":"KnotAli: informed energy minimization through the use of evolutionary information","year":2022,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pseudoknot; Multiple sequence alignment; Computer science; Structural alignment; Sequence alignment; Energy minimization; Nucleic acid structure; Sequence (biology); Nucleic acid secondary structure; Quality Score; Data mining; Computational biology; Bioinformatics; Artificial intelligence; RNA; Biology; Genetics; Physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004885907,0.0001142194,0.0001012455,0.00005018911,0.0002518456,0.00003165081,0.00024168,0.00007413296,0.00009131477],"category_scores_gemma":[0.000184144,0.00008988666,0.00008879358,0.0001782918,0.00006025234,0.0000834547,0.0002440645,0.00005267049,0.000006694551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002626385,"about_ca_system_score_gemma":0.0002026314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003683388,"about_ca_topic_score_gemma":0.000008700945,"domain_scores_codex":[0.9989484,0.00007030613,0.0004783654,0.0000605051,0.0002883766,0.0001540449],"domain_scores_gemma":[0.999119,0.00005076434,0.0003474239,0.0003609714,0.00009612636,0.00002565223],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001022516,0.0002617156,0.00129179,0.0005714385,0.0002904822,6.373862e-7,0.007025374,0.2334892,0.004178546,0.05498343,0.6390674,0.0578175],"study_design_scores_gemma":[0.0002684244,0.0001766533,0.0001375152,0.000006303726,0.00001484794,0.00001885407,0.0009940452,0.03188113,0.008705048,0.0002228371,0.9574416,0.00013275],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07049646,0.001248437,0.9049678,0.0008981038,0.002196271,0.002590709,0.0014826,0.0001630816,0.01595658],"genre_scores_gemma":[0.4159748,0.0009129317,0.5651442,0.007063243,0.0002977694,0.0007183081,0.007167552,0.00006465777,0.002656612],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3454783,"threshold_uncertainty_score":0.3665472,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03025220006781608,"score_gpt":0.2297713294782679,"score_spread":0.1995191294104519,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}